Work

Development of seasonally clustered building models for applying model-based predictive control in buildings

Year: 2025

Type: article

Abstract: Grey-box modeling is commonly applied in model-based predictive control (MPC) for managing building heating, ventilation, and air conditioning operations. However, typical grey-box models are time-invariant, requiring multiple model sets to account for seasonal variations. To address this limitation, we introduce a novel grey-box modeling method that incorporates K-means clustering techniques. Weekly grey-box models were established using measurement data from a department store in South Korea. These 22 model sets were subjected to clustering to group similar models. The performance of the proposed clustered model in predicting indoor air temperature was compared with that of two baseline models: (1) a single-week-based model (Baseline 1), and (2) a monthly-updated model (Baseline 2). The clustered model achieved up to a 70% reduction in the root-mean-square error, demonstrating improved predictive accuracy. Lastly, MPC simulations using the clustered model were conducted for the summer (July and August). The electricity consumption and operating costs were reduced by 6.74% and 36.26% in July and 2.59% and 30.47% in August, respectively compared to the baseline cases. These results validate the applicability of the proposed clustering-based modeling approach for MPC implementation.

Source: Case Studies in Thermal Engineering

DOI: 10.1016/j.csite.2025.107248

Authors: Semi Park, Abu Talib, Kwangwon Choi, Jaewan Joe, Jungkyu Park

Institution: Inha University

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Climate action, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,620